Author

Natsuki Iwano

Waseda UniversityORCID 0000-0001-5603-8903Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Assay:DNA methylation· 1 Organism:E. coli· 1 Assay:long-read sequencing· 1 Approach:machine learning (classical)· 1 Assay:proteomics· 1 Approach:statistical model· 1

Frequent Co-Authors

Michiaki HamadaiD· 3 shared Tsukasa FukunagaiD· 2 shared Hitoshi IuchiiD· 1 shared Kaisei Hara· 1 shared Kazuteru Aoki· 1 shared Keisuke YamadaiD· 1 shared Shion HosodaiD· 1 shared Shitao ZhaoiD· 1 shared

Activity by Year

2021-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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3 shared papersBioinformatics

Michiaki HamadaORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

BioinformaticsFrontiers in BioinformaticsPeer-reviewedOct 10, 2023

DeepRaccess: High-speed RNA accessibility prediction using deep learning

Kaisei Hara, Natsuki Iwano, Tsukasa Fukunaga, Michiaki Hamada

DeepRaccess predicts RNA accessibility using deep learning, achieving high correlation with Raccess and significant speed improvements.

5 citations

E. colilong-read sequencing

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BioinformaticsbioRxivPreprintFeb 17, 2021

RaptGen: A variational autoencoder with profile hidden Markov model for generative aptamer discovery

Natsuki Iwano, Tatsuo Adachi, Kazuteru Aoki…·Michiaki Hamada

RaptGen generates aptamers using a variational autoencoder with a profile hidden Markov model decoder, showing effectiveness in creating novel sequences not present in training data.

3 citations

variational autoencoder

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BioinformaticsComputational and Structural Biotechnology JournalPeer-reviewedJan 1, 2021

Representation learning applications in biological sequence analysis

Hitoshi Iuchi, Taro Matsutani, Keisuke Yamada…·Michiaki Hamada

This paper reviews the application of representation learning in biological sequence analysis, focusing on how embedding techniques transform sequences for functional and structural insights.

83 citations

DNA methylationproteomics

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